TidyVerse - Data visualization

# Load the knitr and kableExtra packages
library(knitr)
library(kableExtra)
options(knitr.table.format = "html")
# Load the gapminder package
library(gapminder)
# Load the dpylr package
library(dplyr)
# Load the ggplot2 package as well
library(ggplot2)
theme_set(theme_bw())  # pre-set the bw theme.

# Create gapminder_1952
gapminder_1952 <- gapminder %>%
    filter(year == 1952)

# Look at the gapminder_1952 dataset
gapminder_1952 %>%
  kable(caption = "Gapminder from 1952") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T, position = "left", , font_size = 11) %>%
  row_spec(0, bold = T, color = "white", background = "#3f7689") %>%
  scroll_box(width = "100%", height = "300px")

ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
  geom_point() + 
  geom_smooth(method="loess", se=F) +
  labs(subtitle="GDP by capita by population", 
       y="GDP per capita", 
       x="Population", 
       title="Scatterplot", 
       caption = "")

# Create a scatter plot with pop on the x-axis and lifeExp on the y-axis
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
  geom_point()+
  geom_smooth(method="loess", se=F) +
  labs(subtitle="Country's population with its life expectancy in the year 1952", 
       y="Life Expectancy", 
       x="Population", 
       title="Scatterplot", 
       caption = "")

# Change this plot to put the x-axis on a log scale
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) +
  geom_point()+
  scale_x_log10() +
  geom_smooth(method="loess", se=F) +
  labs(subtitle="Country's population (passed into log scale) with its life expectancy in the year 1952", 
       y="Life Expectancy", 
       x="Population", 
       title="Scatterplot", 
       caption = "")

# Scatter plot comparing pop and gdpPercap, with both axes on a log scale
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) +
  geom_point() +
  scale_x_log10() +
  scale_y_log10() + 
  geom_smooth(method="loess", se=F) +
  labs(subtitle="Country's population (log scale) with GDP by capita (log scale) in the year 1952", 
       y="GDP by capita", 
       x="Population", 
       title="Scatterplot", 
       caption = "")

# Scatter plot comparing pop and lifeExp, with color representing continent
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent))+
  geom_point() +
  scale_x_log10() +
  labs(subtitle="Country's population (log scale) with Life expectancy in the year 1952", 
       y="Life expectancy", 
       x="Population", 
       title="Scatterplot colored by continent", 
       caption = "")

# Add the size aesthetic to represent a country's gdpPercap
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent, size = gdpPercap)) +
  geom_point() +
  scale_x_log10() +
  labs(subtitle="Country's population (log scale) with Life expectancy in the year 1952", 
       y="Life expectancy", 
       x="Population", 
       title="Scatterplot colored by continent, size by GDB by capita", 
       caption = "")

# Scatter plot comparing pop and lifeExp, faceted by continent
ggplot(gapminder_1952, aes(x = pop, y =lifeExp)) +
  geom_point() +
  scale_x_log10() +
  facet_wrap(~ continent) +
  labs(subtitle="Country's population (log scale) with Life expectancy in the year 1952 by Continent", 
       y="Life expectancy", 
       x="Population", 
       title="Scatterplot of each continent", 
       caption = "")

# Scatter plot comparing gdpPercap and lifeExp, with color representing continent
# and size representing population, faceted by year
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) + 
  geom_point() +
  scale_x_log10() + 
  facet_wrap(~ year) +
  labs(subtitle="GDB per capita (log scale) with Life expectancy by Continent and size population", 
       y="Life expectancy", 
       x="GDP per Capita", 
       title="Scatterplot, every 5 years from 1952 to 2007", 
       caption = "")